3D Parametric Human Recovery.
HuMMan provides SMPL annotations, RGB and RGB-D sequences. Hence, we evaluate HMR [1]}, not only one of the first deep learning approaches towards 3D parametric human recovery but a fundamental component for follow-up works [2]}, [3]}, to represent image-based methods. In addition, we employ VoteHMR [4]}, a recent work that takes point clouds as the input. In Table REF , we find that HMR has achieved low MPJPE and PA-MPJPE, which may be attributed to the clearly defined action set and the training set already includes all action classes. However, VoteHMR is not performing well. We argue that existing point cloud-based methods [5]}, [6]}, [4]} rely heavily on synthetic data for training and evaluation, whereas HuMMan provides genuine point clouds from commercial RGB-D sensors that remain challenging.
<FIGURE>